TR2020-143

Interactive Tactile Perception for Classification of Novel Object Instances


    •  Corcodel, R., Jain, S., van Baar, J., "Interactive Tactile Perception for Classification of Novel Object Instances", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2020.
      BibTeX TR2020-143 PDF
      • @inproceedings{Corcodel2020nov,
      • author = {Corcodel, Radu and Jain, Siddarth and van Baar, Jeroen},
      • title = {Interactive Tactile Perception for Classification of Novel Object Instances},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-143}
      • }
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  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning, Robotics

In this paper, we present a novel approach for classification of unseen object instances from interactive tactile feedback. Furthermore, we demonstrate the utility of a low resolution tactile sensor array for tactile perception that can potentially close the gap between vision and physical contact for manipulation. We contrast our sensor to high-resolution camera-based tactile sensors. Our proposed approach interactively learns a one-class classification model using 3D tactile descriptors, and thus demonstrates an advantage over the existing approaches, which require pre-training on objects. We describe how we derive 3D features from the tactile sensor inputs, and exploit them for learning one-class classifiers. In addition, since our proposed method uses unsupervised learning, we do not require ground truth labels. This makes our proposed method flexible and more practical for deployment on robotic systems. We validate our proposed method on a set of household objects and results indicate good classification performance in real-world experiments